Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Fuzzy multicriteria decision-making : models, methods and applications / / Witold Pedrycz, Petr Ekel and Roberta Parreiras
| Fuzzy multicriteria decision-making : models, methods and applications / / Witold Pedrycz, Petr Ekel and Roberta Parreiras |
| Autore | Pedrycz Witold <1953-> |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Chichester, West Sussex, U.K., : Wiley, 2011 |
| Descrizione fisica | 1 online resource (362 p.) |
| Disciplina | 003/.56 |
| Altri autori (Persone) |
EkelPetr
ParreirasRoberta |
| Soggetto topico |
Decision making
Decision making - Mathematical models Fuzzy decision making |
| ISBN |
1-119-95738-9
0-470-97403-6 1-282-88926-5 9786612889264 0-470-97404-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Decision making in system project, planning, operation, and control : motivation, objective, and basic concepts -- Notions and concepts of fuzzy sets : an introduction -- Selected design and processing aspects of fuzzy sets -- Continuous models of multicriteria decision making and their analysis -- Introduction to preference modeling with binary fuzzy relations -- Construction of fuzzy preference relations -- Discrete models of multicriteria decision making and their analysis -- Generalization of a classic approach to dealing with uncertainty of information for multicriteria decision problems -- Group decision-making : fuzzy models -- Use of consensus schemes in group decision making. |
| Record Nr. | UNINA-9910140914003321 |
Pedrycz Witold <1953->
|
||
| Chichester, West Sussex, U.K., : Wiley, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Fuzzy systems engineering : toward human-centric computing / / Witold Pedrycz, Fernando Gomide
| Fuzzy systems engineering : toward human-centric computing / / Witold Pedrycz, Fernando Gomide |
| Autore | Pedrycz Witold <1953-> |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley : , c2007 |
| Descrizione fisica | 1 online resource (550 p.) |
| Disciplina |
006.3
620.00113 |
| Altri autori (Persone) | GomideFernando |
| Soggetto topico |
Soft computing
Fuzzy systems |
| ISBN |
1-281-00192-9
9786611001926 0-470-16896-X 0-470-16895-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface -- 1 Introduction -- 1.1 Digital communities and a fundamental quest for human-centric systems -- 1.2 A historical overview: towards a non-Aristotelian perspective of the world -- 1.3 Granular Computing -- 1.4 Quantifying information granularity: generality versus specificity -- 1.5 Computational Intelligence -- 1.6 Granular Computing and Computational Intelligence -- 1.7 Conclusions -- Exercises and problems -- Historical notes -- References -- 2 Notions and Concepts of Fuzzy Sets -- 2.1 Sets and fuzzy sets: a departure from the principle of dichotomy -- 2.2 Interpretation of fuzzy sets -- 2.3 Membership functions and their motivation -- 2.4 Fuzzy numbers and intervals -- 2.5 Linguistic variables -- 2.6 Conclusions -- Exercises and problems -- Historical notes -- References -- 3 Characterization of Fuzzy Sets -- 3.1 A generic characterization of fuzzy sets: some fundamental descriptors -- 3.2 Equality and inclusion relationships in fuzzy sets -- 3.3 Energy and entropy measures of fuzziness -- 3.4 Specificity of fuzzy sets -- 3.5 Geometric interpretation of sets and fuzzy sets -- 3.6 Granulation of information -- 3.7 Characterization of the families of fuzzy sets -- 3.8 Fuzzy sets, sets, and the representation theorem -- 3.9 Conclusions -- Exercises and problems -- Historical notes -- References -- 4 The Design of Fuzzy Sets -- 4.1 Semantics of fuzzy sets: some general observations -- 4.2 Fuzzy set as a descriptor of feasible solutions -- 4.3 Fuzzy set as a descriptor of the notion of typicality -- 4.4 Membership functions in the visualization of preferences of solutions -- 4.5 Nonlinear transformation of fuzzy sets -- 4.6 Vertical and horizontal schemes of membership estimation -- 4.7 Saaty's priority method of pairwise membership function estimation -- 4.8 Fuzzy sets as granular representatives of numeric data -- 4.9 From numeric data to fuzzy sets -- 4.10 Fuzzy equalization -- 4.11 Linguistic approximation.
4.12 Design guidelines for the construction of fuzzy sets -- 4.13 Conclusions -- Exercises and problems -- Historical notes -- References -- 5 Operations and Aggregations of Fuzzy Sets -- 5.1 Standard operations on sets and fuzzy sets -- 5.2 Generic requirements for operations on fuzzy sets -- 5.3 Triangular norms -- 5.4 Triangular conorms -- 5.5 Triangular norms as a general category of logical operators -- 5.6 Aggregation operations -- 5.7 Fuzzy measure and integral -- 5.8 Negations -- 5.9 Conclusions -- Exercises and problems -- Historical notes -- References -- 6 Fuzzy Relations -- 6.1 The concept of relations -- 6.2 Fuzzy relations -- 6.3 Properties of the fuzzy relations -- 6.4 Operations on fuzzy relations -- 6.5 Cartesian product, projections and cylindrical extension of fuzzy sets -- 6.6 Reconstruction of fuzzy relations -- 6.7 Binary fuzzy relations -- 6.8 Conclusions -- Exercises and problems -- Historical notes -- References -- 7 Transformations of Fuzzy Sets -- 7.1 The extension principle -- 7.2 Compositions of fuzzy relations -- 7.3 Fuzzy relational equations -- 7.4 Associative Memories -- 7.5 Fuzzy numbers and fuzzy arithmetic -- 7.6 Conclusions -- Exercises and problems -- Historical notes -- References -- 8 Generalizations and Extensions of Fuzzy Sets -- 8.1 Fuzzy sets of higher order -- 8.2 Rough fuzzy sets and fuzzy rough sets -- 8.3 Interval-valued fuzzy sets -- 8.4 Type-2 fuzzy sets -- 8.5 Shadowed sets as a three-valued logic characterization of fuzzy sets -- 8.6 Probability and fuzzy sets -- 8.7 Probability of fuzzy events -- 8.8 Conclusions -- Exercises and problems -- Historical notes -- References -- 9 Interoperability Aspects of Fuzzy Sets -- 9.1 Fuzzy set and its family of s-cuts -- 9.2 Fuzzy sets and their interfacing with the external world -- 9.3 Encoding and decoding as an optimization problem of vector quantization -- 9.4 Decoding of a fuzzy set through a family of fuzzy sets. 9.5 Taxonomy of data in structure description with shadowed sets -- 9.6 Conclusions -- Exercises and problems -- Historical notes -- References -- 10. Fuzzy Modeling: Principles and Methodology -- 10.1 The architectural blueprint of fuzzy models -- 10.2 Key phases of the development and use of fuzzy models -- 10.3 Main categories of fuzzy models: an overview -- 10.4 Verification and validation of fuzzy models -- 10.5 Conclusions -- Exercises and problems -- Historical notes -- References -- 11 Rule-based Fuzzy Models -- 11.1 Fuzzy rules as a vehicle of knowledge representation -- 11.2 General categories of fuzzy rules and their semantics -- 11.3 Syntax of fuzzy rules -- 11.4 Basic Functional Modules: Rule base, Database, and Inference scheme -- 11.5 Types of Rule-Based Systems and Architectures -- 11.6 Approximation properties of fuzzy rule-based models -- 11.7 Development of Rule-Based Systems -- 11.8 Parameter estimation procedure for functional rule-based systems -- 11.9 Design issues of rule-based systems - consistency, completeness, and the curse of dimensionality -- 11.10 The curse of dimensionality in rule-based systems -- 11.11 Development scheme of fuzzy rule-based models -- 11.12 Conclusions -- Exercises and problems -- Historical notes -- References -- 12 From Logic Expressions to Fuzzy Logic Networks -- 12.1 Introduction -- 12.2 Main categories of fuzzy neurons -- 12.3 Uninorm-based fuzzy neurons -- 12.4 Architectures of logic networks -- 12.5 The development mechanisms of the fuzzy neural networks -- 12.6 Interpretation of the fuzzy neural networks -- 12.7 From fuzzy logic networks to Boolean functions and their minimization through learning -- 12.8 Interfacing the fuzzy neural network -- 12.9 Interpretation aspects - a refinement of induced rule-based system -- 12.10 Reconciliation of perception of information granules and granular mappings -- 12.11 Conclusions -- Exercises and problems -- Historical notes. References -- 13. Fuzzy Systems and Computational Intelligence -- 13.1 Computational Intelligence -- 13.2 Recurrent neurofuzzy systems -- 13.3 Genetic fuzzy systems -- 13.4 Coevolutionary hierarchical genetic fuzzy system -- 13.5 Hierarchical collaborative relations -- 13.6 Evolving fuzzy systems -- 13.7 Conclusions -- Exercises and problems -- Historical notes -- References -- 14. Granular Models and Human Centric Computing -- 14.1 The cluster-based representation of the input - output mappings -- 14.2 Context-based clustering in the development of granular models -- 14.3 Granular neuron as a generic processing element in granular networks -- 14.4 Architecture of granular models based on conditional fuzzy clustering -- 14.5 Refinements of granular models -- 14.6 Incremental granular models -- 14.7 Human-centric fuzzy clustering -- 14.8 Participatory learning in fuzzy clustering -- 14.9 Conclusions -- Exercises and problems -- Historical notes -- References -- 15. Emerging Trends in Fuzzy Systems -- 15.1 Relational ontology in information retrieval -- 15.2 Multiagent fuzzy systems -- 15.3 Distributed fuzzy control -- 15.4 Conclusions -- Exercises and problems -- Historical notes -- References -- Appendix A: Mathematical Prerequisites -- Appendix B: Neurocomputing -- Appendix C: Biologically Inspired Optimization -- Index. |
| Record Nr. | UNINA-9910144575803321 |
|
Pedrycz Witold <1953->
|
||
| Hoboken, New Jersey : , : John Wiley : , c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Fuzzy systems engineering : toward human-centric computing / / Witold Pedrycz, Fernando Gomide
| Fuzzy systems engineering : toward human-centric computing / / Witold Pedrycz, Fernando Gomide |
| Autore | Pedrycz Witold <1953-> |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley : , c2007 |
| Descrizione fisica | 1 online resource (550 p.) |
| Disciplina |
006.3
620.00113 |
| Altri autori (Persone) | GomideFernando |
| Soggetto topico |
Soft computing
Fuzzy systems |
| ISBN |
1-281-00192-9
9786611001926 0-470-16896-X 0-470-16895-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface -- 1 Introduction -- 1.1 Digital communities and a fundamental quest for human-centric systems -- 1.2 A historical overview: towards a non-Aristotelian perspective of the world -- 1.3 Granular Computing -- 1.4 Quantifying information granularity: generality versus specificity -- 1.5 Computational Intelligence -- 1.6 Granular Computing and Computational Intelligence -- 1.7 Conclusions -- Exercises and problems -- Historical notes -- References -- 2 Notions and Concepts of Fuzzy Sets -- 2.1 Sets and fuzzy sets: a departure from the principle of dichotomy -- 2.2 Interpretation of fuzzy sets -- 2.3 Membership functions and their motivation -- 2.4 Fuzzy numbers and intervals -- 2.5 Linguistic variables -- 2.6 Conclusions -- Exercises and problems -- Historical notes -- References -- 3 Characterization of Fuzzy Sets -- 3.1 A generic characterization of fuzzy sets: some fundamental descriptors -- 3.2 Equality and inclusion relationships in fuzzy sets -- 3.3 Energy and entropy measures of fuzziness -- 3.4 Specificity of fuzzy sets -- 3.5 Geometric interpretation of sets and fuzzy sets -- 3.6 Granulation of information -- 3.7 Characterization of the families of fuzzy sets -- 3.8 Fuzzy sets, sets, and the representation theorem -- 3.9 Conclusions -- Exercises and problems -- Historical notes -- References -- 4 The Design of Fuzzy Sets -- 4.1 Semantics of fuzzy sets: some general observations -- 4.2 Fuzzy set as a descriptor of feasible solutions -- 4.3 Fuzzy set as a descriptor of the notion of typicality -- 4.4 Membership functions in the visualization of preferences of solutions -- 4.5 Nonlinear transformation of fuzzy sets -- 4.6 Vertical and horizontal schemes of membership estimation -- 4.7 Saaty's priority method of pairwise membership function estimation -- 4.8 Fuzzy sets as granular representatives of numeric data -- 4.9 From numeric data to fuzzy sets -- 4.10 Fuzzy equalization -- 4.11 Linguistic approximation.
4.12 Design guidelines for the construction of fuzzy sets -- 4.13 Conclusions -- Exercises and problems -- Historical notes -- References -- 5 Operations and Aggregations of Fuzzy Sets -- 5.1 Standard operations on sets and fuzzy sets -- 5.2 Generic requirements for operations on fuzzy sets -- 5.3 Triangular norms -- 5.4 Triangular conorms -- 5.5 Triangular norms as a general category of logical operators -- 5.6 Aggregation operations -- 5.7 Fuzzy measure and integral -- 5.8 Negations -- 5.9 Conclusions -- Exercises and problems -- Historical notes -- References -- 6 Fuzzy Relations -- 6.1 The concept of relations -- 6.2 Fuzzy relations -- 6.3 Properties of the fuzzy relations -- 6.4 Operations on fuzzy relations -- 6.5 Cartesian product, projections and cylindrical extension of fuzzy sets -- 6.6 Reconstruction of fuzzy relations -- 6.7 Binary fuzzy relations -- 6.8 Conclusions -- Exercises and problems -- Historical notes -- References -- 7 Transformations of Fuzzy Sets -- 7.1 The extension principle -- 7.2 Compositions of fuzzy relations -- 7.3 Fuzzy relational equations -- 7.4 Associative Memories -- 7.5 Fuzzy numbers and fuzzy arithmetic -- 7.6 Conclusions -- Exercises and problems -- Historical notes -- References -- 8 Generalizations and Extensions of Fuzzy Sets -- 8.1 Fuzzy sets of higher order -- 8.2 Rough fuzzy sets and fuzzy rough sets -- 8.3 Interval-valued fuzzy sets -- 8.4 Type-2 fuzzy sets -- 8.5 Shadowed sets as a three-valued logic characterization of fuzzy sets -- 8.6 Probability and fuzzy sets -- 8.7 Probability of fuzzy events -- 8.8 Conclusions -- Exercises and problems -- Historical notes -- References -- 9 Interoperability Aspects of Fuzzy Sets -- 9.1 Fuzzy set and its family of s-cuts -- 9.2 Fuzzy sets and their interfacing with the external world -- 9.3 Encoding and decoding as an optimization problem of vector quantization -- 9.4 Decoding of a fuzzy set through a family of fuzzy sets. 9.5 Taxonomy of data in structure description with shadowed sets -- 9.6 Conclusions -- Exercises and problems -- Historical notes -- References -- 10. Fuzzy Modeling: Principles and Methodology -- 10.1 The architectural blueprint of fuzzy models -- 10.2 Key phases of the development and use of fuzzy models -- 10.3 Main categories of fuzzy models: an overview -- 10.4 Verification and validation of fuzzy models -- 10.5 Conclusions -- Exercises and problems -- Historical notes -- References -- 11 Rule-based Fuzzy Models -- 11.1 Fuzzy rules as a vehicle of knowledge representation -- 11.2 General categories of fuzzy rules and their semantics -- 11.3 Syntax of fuzzy rules -- 11.4 Basic Functional Modules: Rule base, Database, and Inference scheme -- 11.5 Types of Rule-Based Systems and Architectures -- 11.6 Approximation properties of fuzzy rule-based models -- 11.7 Development of Rule-Based Systems -- 11.8 Parameter estimation procedure for functional rule-based systems -- 11.9 Design issues of rule-based systems - consistency, completeness, and the curse of dimensionality -- 11.10 The curse of dimensionality in rule-based systems -- 11.11 Development scheme of fuzzy rule-based models -- 11.12 Conclusions -- Exercises and problems -- Historical notes -- References -- 12 From Logic Expressions to Fuzzy Logic Networks -- 12.1 Introduction -- 12.2 Main categories of fuzzy neurons -- 12.3 Uninorm-based fuzzy neurons -- 12.4 Architectures of logic networks -- 12.5 The development mechanisms of the fuzzy neural networks -- 12.6 Interpretation of the fuzzy neural networks -- 12.7 From fuzzy logic networks to Boolean functions and their minimization through learning -- 12.8 Interfacing the fuzzy neural network -- 12.9 Interpretation aspects - a refinement of induced rule-based system -- 12.10 Reconciliation of perception of information granules and granular mappings -- 12.11 Conclusions -- Exercises and problems -- Historical notes. References -- 13. Fuzzy Systems and Computational Intelligence -- 13.1 Computational Intelligence -- 13.2 Recurrent neurofuzzy systems -- 13.3 Genetic fuzzy systems -- 13.4 Coevolutionary hierarchical genetic fuzzy system -- 13.5 Hierarchical collaborative relations -- 13.6 Evolving fuzzy systems -- 13.7 Conclusions -- Exercises and problems -- Historical notes -- References -- 14. Granular Models and Human Centric Computing -- 14.1 The cluster-based representation of the input - output mappings -- 14.2 Context-based clustering in the development of granular models -- 14.3 Granular neuron as a generic processing element in granular networks -- 14.4 Architecture of granular models based on conditional fuzzy clustering -- 14.5 Refinements of granular models -- 14.6 Incremental granular models -- 14.7 Human-centric fuzzy clustering -- 14.8 Participatory learning in fuzzy clustering -- 14.9 Conclusions -- Exercises and problems -- Historical notes -- References -- 15. Emerging Trends in Fuzzy Systems -- 15.1 Relational ontology in information retrieval -- 15.2 Multiagent fuzzy systems -- 15.3 Distributed fuzzy control -- 15.4 Conclusions -- Exercises and problems -- Historical notes -- References -- Appendix A: Mathematical Prerequisites -- Appendix B: Neurocomputing -- Appendix C: Biologically Inspired Optimization -- Index. |
| Record Nr. | UNINA-9910830241003321 |
|
Pedrycz Witold <1953->
|
||
| Hoboken, New Jersey : , : John Wiley : , c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Granular computing : analysis and design of intelligent systems / / Witold Pedrycz
| Granular computing : analysis and design of intelligent systems / / Witold Pedrycz |
| Autore | Pedrycz Witold <1953-> |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Boca Raton : , : Taylor & Francis, , 2013 |
| Descrizione fisica | 1 online resource (295 p.) |
| Disciplina | 006.3 |
| Collana | Industrial electronics series |
| Soggetto topico | Granular computing |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-351-83262-X
1-315-21673-6 1-4398-8687-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Contents; Preface; The Author; Foreword; Chapter 1 - Information Granularity, Information Granules, and Granular Computing; Chapter 2 - Key Formalisms for Representation of Information Granules and Processing Mechanisms; Chapter 3 - Information Granules of Higher Type and Higher Order, and Hybrid Information Granules; Chapter 4 - Representation of Information Granules; Chapter 5 - The Design of Information Granules; Chapter 6 - Optimal Allocation of Information Granularity: Building Granular Mappings; Chapter 7 - Granular Description of Data and Pattern Classification
Chapter 8 - Granular Models: Architectures and DevelopmentChapter 9 - Granular Time Series; Chapter 10 - From Models to Granular Models; Chapter 11 - Collaborative and Linguistic Models of Decision Making; Back Cover |
| Record Nr. | UNINA-9910463151403321 |
|
Pedrycz Witold <1953->
|
||
| Boca Raton : , : Taylor & Francis, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Granular computing : analysis and design of intelligent systems / / Witold Pedrycz
| Granular computing : analysis and design of intelligent systems / / Witold Pedrycz |
| Autore | Pedrycz Witold <1953-> |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Boca Raton : , : Taylor & Francis, , 2013 |
| Descrizione fisica | 1 online resource (295 p.) |
| Disciplina | 006.3 |
| Collana | Industrial electronics series |
| Soggetto topico | Granular computing |
| ISBN |
1-351-83262-X
1-315-21673-6 1-4398-8687-3 |
| Classificazione | COM051240TEC008000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Contents; Preface; The Author; Foreword; Chapter 1 - Information Granularity, Information Granules, and Granular Computing; Chapter 2 - Key Formalisms for Representation of Information Granules and Processing Mechanisms; Chapter 3 - Information Granules of Higher Type and Higher Order, and Hybrid Information Granules; Chapter 4 - Representation of Information Granules; Chapter 5 - The Design of Information Granules; Chapter 6 - Optimal Allocation of Information Granularity: Building Granular Mappings; Chapter 7 - Granular Description of Data and Pattern Classification
Chapter 8 - Granular Models: Architectures and DevelopmentChapter 9 - Granular Time Series; Chapter 10 - From Models to Granular Models; Chapter 11 - Collaborative and Linguistic Models of Decision Making; Back Cover |
| Record Nr. | UNINA-9910786732903321 |
|
Pedrycz Witold <1953->
|
||
| Boca Raton : , : Taylor & Francis, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Granular computing : analysis and design of intelligent systems / / Witold Pedrycz
| Granular computing : analysis and design of intelligent systems / / Witold Pedrycz |
| Autore | Pedrycz Witold <1953-> |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Boca Raton : , : Taylor & Francis, , 2013 |
| Descrizione fisica | 1 online resource (295 p.) |
| Disciplina | 006.3 |
| Collana | Industrial electronics series |
| Soggetto topico | Granular computing |
| ISBN |
1-351-83262-X
1-315-21673-6 1-4398-8687-3 |
| Classificazione | COM051240TEC008000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Contents; Preface; The Author; Foreword; Chapter 1 - Information Granularity, Information Granules, and Granular Computing; Chapter 2 - Key Formalisms for Representation of Information Granules and Processing Mechanisms; Chapter 3 - Information Granules of Higher Type and Higher Order, and Hybrid Information Granules; Chapter 4 - Representation of Information Granules; Chapter 5 - The Design of Information Granules; Chapter 6 - Optimal Allocation of Information Granularity: Building Granular Mappings; Chapter 7 - Granular Description of Data and Pattern Classification
Chapter 8 - Granular Models: Architectures and DevelopmentChapter 9 - Granular Time Series; Chapter 10 - From Models to Granular Models; Chapter 11 - Collaborative and Linguistic Models of Decision Making; Back Cover |
| Record Nr. | UNINA-9910825463503321 |
|
Pedrycz Witold <1953->
|
||
| Boca Raton : , : Taylor & Francis, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Knowledge-based clustering [[electronic resource] ] : from data to information granules / / Witold Pedrycz
| Knowledge-based clustering [[electronic resource] ] : from data to information granules / / Witold Pedrycz |
| Autore | Pedrycz Witold <1953-> |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2005 |
| Descrizione fisica | 1 online resource (336 p.) |
| Disciplina | 006.3 |
| Soggetto topico |
Soft computing
Granular computing Fuzzy systems |
| ISBN |
1-280-27547-2
9786610275472 0-470-24355-4 0-471-70859-3 0-471-70860-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
KNOWLEDGE-BASED CLUSTERING; Contents; Foreword; Preface; 1 Clustering and Fuzzy Clustering; 1.1 Introduction; 1.2 Basic Notions and Notation; 1.2.1 Types of Data; 1.2.2 Distance and Similarity; 1.3 Main Categories of Clustering Algorithms; 1.3.1 Hierarchical Clustering; 1.3.2 Objective Function-Based Clustering; 1.4 Clustering and Classification; 1.5 Fuzzy Clustering; 1.6 Cluster Validity; 1.7 Extensions of Objective Function-Based Fuzzy Clustering; 1.7.1 Augmented Geometry of Fuzzy Clusters: Fuzzy C Varieties; 1.7.2 Possibilistic Clustering; 1.7.3 Noise Clustering
1.8 Self-Organizing Maps and Fuzzy Objective Function-Based Clustering1.9 Conclusions; References; 2 Computing with Granular Information: Fuzzy Sets and Fuzzy Relations; 2.1 A Paradigm of Granular Computing: Information Granules and Their Processing; 2.2 Fuzzy Sets as Human-Centric Information Granules; 2.3 Operations on Fuzzy Sets; 2.4 Fuzzy Relations; 2.5 Comparison of Two Fuzzy Sets; 2.6 Generalizations of Fuzzy Sets; 2.7 Shadowed Sets; 2.8 Rough Sets; 2.9 Granular Computing and Distributed Processing; 2.10 Conclusions; References; 3 Logic-Oriented Neurocomputing; 3.1 Introduction 3.2 Main Categories of Fuzzy Neurons3.2.1 Aggregative Neurons; 3.2.2 Referential (Reference) Neurons; 3.3 Architectures of Logic Networks; 3.4 Interpretation Aspects of the Networks; 3.5 Granular Interfaces of Logic Processing; 3.6 Conclusions; References; 4 Conditional Fuzzy Clustering; 4.1 Introduction; 4.2 Problem Statement: Context Fuzzy Sets and Objective Function; 4.3 The Optimization Problem; 4.4 Computational Considerations of Conditional Clustering; 4.5 Generalizations of the Algorithm Through the Aggregation Operator; 4.6 Fuzzy Clustering with Spatial Constraints; 4.7 Conclusions References5 Clustering with Partial Supervision; 5.1 Introduction; 5.2 Problem Formulation; 5.3 Design of the Clusters; 5.4 Experimental Examples; 5.5 Cluster-Based Tracking Problem; 5.6 Conclusions; References; 6 Principles of Knowledge-Based Guidance in Fuzzy Clustering; 6.1 Introduction; 6.2 Examples of Knowledge-Oriented Hints and Their General Taxonomy; 6.3 The Optimization Environment of Knowledge-Enhanced Clustering; 6.4 Quantification of Knowledge-Based Guidance Hints and Their Optimization; 6.5 Organization of the Interaction Process; 6.6 Proximity-Based Clustering (P-FCM) 6.7 Web Exploration and P-FCM6.8 Linguistic Augmentation of Knowledge-Based Hints; 6.9 Conclusions; References; 7 Collaborative Clustering; 7.1 Introduction and Rationale; 7.2 Horizontal and Vertical Clustering; 7.3 Horizontal Collaborative Clustering; 7.3.1 Optimization Details; 7.3.2 The Flow of Computing of Collaborative Clustering; 7.3.3 Quantification of the Collaborative Phenomenon of Clustering; 7.4 Experimental Studies; 7.5 Further Enhancements of Horizontal Clustering; 7.6 The Algorithm of Vertical Clustering; 7.7 A Grid Model of Horizontal and Vertical Clustering 7.8 Consensus Clustering |
| Record Nr. | UNINA-9910146055403321 |
|
Pedrycz Witold <1953->
|
||
| Hoboken, N.J., : Wiley, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Knowledge-based clustering [[electronic resource] ] : from data to information granules / / Witold Pedrycz
| Knowledge-based clustering [[electronic resource] ] : from data to information granules / / Witold Pedrycz |
| Autore | Pedrycz Witold <1953-> |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2005 |
| Descrizione fisica | 1 online resource (336 p.) |
| Disciplina | 006.3 |
| Soggetto topico |
Soft computing
Granular computing Fuzzy systems |
| ISBN |
1-280-27547-2
9786610275472 0-470-24355-4 0-471-70859-3 0-471-70860-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
KNOWLEDGE-BASED CLUSTERING; Contents; Foreword; Preface; 1 Clustering and Fuzzy Clustering; 1.1 Introduction; 1.2 Basic Notions and Notation; 1.2.1 Types of Data; 1.2.2 Distance and Similarity; 1.3 Main Categories of Clustering Algorithms; 1.3.1 Hierarchical Clustering; 1.3.2 Objective Function-Based Clustering; 1.4 Clustering and Classification; 1.5 Fuzzy Clustering; 1.6 Cluster Validity; 1.7 Extensions of Objective Function-Based Fuzzy Clustering; 1.7.1 Augmented Geometry of Fuzzy Clusters: Fuzzy C Varieties; 1.7.2 Possibilistic Clustering; 1.7.3 Noise Clustering
1.8 Self-Organizing Maps and Fuzzy Objective Function-Based Clustering1.9 Conclusions; References; 2 Computing with Granular Information: Fuzzy Sets and Fuzzy Relations; 2.1 A Paradigm of Granular Computing: Information Granules and Their Processing; 2.2 Fuzzy Sets as Human-Centric Information Granules; 2.3 Operations on Fuzzy Sets; 2.4 Fuzzy Relations; 2.5 Comparison of Two Fuzzy Sets; 2.6 Generalizations of Fuzzy Sets; 2.7 Shadowed Sets; 2.8 Rough Sets; 2.9 Granular Computing and Distributed Processing; 2.10 Conclusions; References; 3 Logic-Oriented Neurocomputing; 3.1 Introduction 3.2 Main Categories of Fuzzy Neurons3.2.1 Aggregative Neurons; 3.2.2 Referential (Reference) Neurons; 3.3 Architectures of Logic Networks; 3.4 Interpretation Aspects of the Networks; 3.5 Granular Interfaces of Logic Processing; 3.6 Conclusions; References; 4 Conditional Fuzzy Clustering; 4.1 Introduction; 4.2 Problem Statement: Context Fuzzy Sets and Objective Function; 4.3 The Optimization Problem; 4.4 Computational Considerations of Conditional Clustering; 4.5 Generalizations of the Algorithm Through the Aggregation Operator; 4.6 Fuzzy Clustering with Spatial Constraints; 4.7 Conclusions References5 Clustering with Partial Supervision; 5.1 Introduction; 5.2 Problem Formulation; 5.3 Design of the Clusters; 5.4 Experimental Examples; 5.5 Cluster-Based Tracking Problem; 5.6 Conclusions; References; 6 Principles of Knowledge-Based Guidance in Fuzzy Clustering; 6.1 Introduction; 6.2 Examples of Knowledge-Oriented Hints and Their General Taxonomy; 6.3 The Optimization Environment of Knowledge-Enhanced Clustering; 6.4 Quantification of Knowledge-Based Guidance Hints and Their Optimization; 6.5 Organization of the Interaction Process; 6.6 Proximity-Based Clustering (P-FCM) 6.7 Web Exploration and P-FCM6.8 Linguistic Augmentation of Knowledge-Based Hints; 6.9 Conclusions; References; 7 Collaborative Clustering; 7.1 Introduction and Rationale; 7.2 Horizontal and Vertical Clustering; 7.3 Horizontal Collaborative Clustering; 7.3.1 Optimization Details; 7.3.2 The Flow of Computing of Collaborative Clustering; 7.3.3 Quantification of the Collaborative Phenomenon of Clustering; 7.4 Experimental Studies; 7.5 Further Enhancements of Horizontal Clustering; 7.6 The Algorithm of Vertical Clustering; 7.7 A Grid Model of Horizontal and Vertical Clustering 7.8 Consensus Clustering |
| Record Nr. | UNINA-9910806144503321 |
|
Pedrycz Witold <1953->
|
||
| Hoboken, N.J., : Wiley, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||